




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


a    				=      logging.get_logger(__name__)

a    				=      {"vocab_file": "sentencepiece.bpe.model"}

a    				=      {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

a    				=      {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

a    				=      "▁"




class    _A					(		UpperCAmelCase__  ):
		__a            =							VOCAB_FILES_NAMES
		__a            =							PRETRAINED_VOCAB_FILES_MAP
		__a            =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__a            =							["input_ids", "attention_mask"]






		def __init__(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE="<s>"    ,		_SCREAMING_SNAKE_CASE="</s>"    ,		_SCREAMING_SNAKE_CASE="</s>"    ,		_SCREAMING_SNAKE_CASE="<s>"    ,		_SCREAMING_SNAKE_CASE="<unk>"    ,		_SCREAMING_SNAKE_CASE="<pad>"    ,		_SCREAMING_SNAKE_CASE="<mask>"    ,		_SCREAMING_SNAKE_CASE = None    ,		**_SCREAMING_SNAKE_CASE    ,		):
								_UpperCAmelCase	     =      AddedToken(_SCREAMING_SNAKE_CASE    ,		lstrip=_SCREAMING_SNAKE_CASE    ,		rstrip=_SCREAMING_SNAKE_CASE      ) if isinstance(_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE      ) else mask_token

								_UpperCAmelCase	     =      {} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=_SCREAMING_SNAKE_CASE    ,		eos_token=_SCREAMING_SNAKE_CASE    ,		unk_token=_SCREAMING_SNAKE_CASE    ,		sep_token=_SCREAMING_SNAKE_CASE    ,		cls_token=_SCREAMING_SNAKE_CASE    ,		pad_token=_SCREAMING_SNAKE_CASE    ,		mask_token=_SCREAMING_SNAKE_CASE    ,		sp_model_kwargs=self.sp_model_kwargs    ,		**_SCREAMING_SNAKE_CASE    ,		)

								_UpperCAmelCase	     =      vocab_file
								_UpperCAmelCase	     =      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(str(_SCREAMING_SNAKE_CASE      )      )

								_UpperCAmelCase	     =      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

								_UpperCAmelCase	     =      len(self.sp_model      ) - 1
								_UpperCAmelCase	     =      {v: k for k, v in self.fairseq_tokens_to_ids.items()}






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None      ):



								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_UpperCAmelCase	     =      [self.cls_token_id]
								_UpperCAmelCase	     =      [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None    ,		_SCREAMING_SNAKE_CASE = False      ):



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_SCREAMING_SNAKE_CASE    ,		token_ids_a=_SCREAMING_SNAKE_CASE    ,		already_has_special_tokens=_SCREAMING_SNAKE_CASE      )

								if token_ids_a is None:
														return [1] + ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1]
								return [1] + ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1, 1] + ([0] * len(_SCREAMING_SNAKE_CASE      )) + [1]






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None      ):
								_UpperCAmelCase	     =      [self.sep_token_id]
								_UpperCAmelCase	     =      [self.cls_token_id]

								if token_ids_a is None:
														return len(cls + token_ids_a + sep      ) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]






		@property
		def 					UpperCAmelCase			(     self      ):
								return len(self.sp_model      )






		def 					UpperCAmelCase			(     self      ):
								_UpperCAmelCase	     =      {self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE      ): i for i in range(self.vocab_size      )}
								vocab.update(self.added_tokens_encoder      )
								return vocab






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE      ):
								return self.sp_model.encode(_SCREAMING_SNAKE_CASE    ,		out_type=_SCREAMING_SNAKE_CASE      )






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE      ):



								if token in self.fairseq_tokens_to_ids:
														return self.fairseq_tokens_to_ids[token]
								_UpperCAmelCase	     =      self.sp_model.PieceToId(_SCREAMING_SNAKE_CASE      )

								return spm_id if spm_id else self.unk_token_id






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE      ):



								if index in self.fairseq_ids_to_tokens:
														return self.fairseq_ids_to_tokens[index]
								return self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE      )






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE      ):
								_UpperCAmelCase	     =      []
								_UpperCAmelCase	     =      """"""
								_UpperCAmelCase	     =      False
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				if not prev_is_special:
																										out_string += " "
																				out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE      ) + token
																				_UpperCAmelCase	     =      True
																				_UpperCAmelCase	     =      []
														else:
																				current_sub_tokens.append(_SCREAMING_SNAKE_CASE      )
																				_UpperCAmelCase	     =      False
								out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE      )
								return out_string.strip()






		def __getstate__(     self      ):
								_UpperCAmelCase	     =      self.__dict__.copy()
								_UpperCAmelCase	     =      None
								return state






		def __setstate__(     self    ,		_SCREAMING_SNAKE_CASE      ):
								_UpperCAmelCase	     =      d

								# for backward compatibility
								if not hasattr(self    ,		"""sp_model_kwargs"""      ):
														_UpperCAmelCase	     =      {}

								_UpperCAmelCase	     =      spm.SentencePieceProcessor(**self.sp_model_kwargs      )
								self.sp_model.Load(self.vocab_file      )






		def 					UpperCAmelCase			(     self    ,		_SCREAMING_SNAKE_CASE    ,		_SCREAMING_SNAKE_CASE = None      ):



								if not os.path.isdir(_SCREAMING_SNAKE_CASE      ):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
														return
								_UpperCAmelCase	     =      os.path.join(
								    _SCREAMING_SNAKE_CASE    ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )

								if os.path.abspath(self.vocab_file      ) != os.path.abspath(_SCREAMING_SNAKE_CASE      ) and os.path.isfile(self.vocab_file      ):
														copyfile(self.vocab_file    ,		_SCREAMING_SNAKE_CASE      )
								elif not os.path.isfile(self.vocab_file      ):
														with open(_SCREAMING_SNAKE_CASE    ,		"""wb"""      ) as fi:
																				_UpperCAmelCase	     =      self.sp_model.serialized_model_proto()
																				fi.write(_SCREAMING_SNAKE_CASE      )

								return (out_vocab_file,)
'''simple docstring'''




import unittest
from typing import Tuple

import torch

from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch




@require_torch
class        a__ :






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         return self.get_dummy_input()






     @property
     def   SCREAMING_SNAKE_CASE__  (					self						:						str       ):





         """simple docstring"""



         if self.block_type == "down":
             return (4, 32, 16, 16)
         elif self.block_type == "mid":
             return (4, 32, 32, 32)
         elif self.block_type == "up":
             return (4, 32, 64, 64)

         raise ValueError(f"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'."""       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[Any]=True    ,							a						:						Dict=False    ,							a						:						Optional[Any]=False    ,							a						:						List[Any]=False    ,							):





         """simple docstring"""



         __lowerCamelCase            =    4
         __lowerCamelCase            =    32
         __lowerCamelCase            =    (32, 32)

         __lowerCamelCase            =    torch.manual_seed(0       )
         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    (batch_size, num_channels) + sizes
         __lowerCamelCase            =    randn_tensor(a    ,							generator=a    ,							device=a       )
         __lowerCamelCase            =    {'''hidden_states''': hidden_states}

         if include_temb:
             __lowerCamelCase            =    1_28
             __lowerCamelCase            =    randn_tensor((batch_size, temb_channels)    ,							generator=a    ,							device=a       )

         if include_res_hidden_states_tuple:
             __lowerCamelCase            =    torch.manual_seed(1       )
             __lowerCamelCase            =    (randn_tensor(a    ,							generator=a    ,							device=a       ),)

         if include_encoder_hidden_states:
             __lowerCamelCase            =    floats_tensor((batch_size, 32, 32)       ).to(a       )

         if include_skip_sample:
             __lowerCamelCase            =    randn_tensor(((batch_size, 3) + sizes)    ,							generator=a    ,							device=a       )

         return dummy_input






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    {
             '''in_channels''': 32,
             '''out_channels''': 32,
             '''temb_channels''': 1_28,
         }
         if self.block_type == "up":
             __lowerCamelCase            =    32

         if self.block_type == "mid":
             init_dict.pop('''out_channels'''       )

         __lowerCamelCase            =    self.dummy_input
         return init_dict, inputs_dict






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         unet_block.to(a       )
         unet_block.eval()

         with torch.no_grad():
             __lowerCamelCase            =    unet_block(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         self.assertEqual(output.shape    ,							self.output_shape       )

         __lowerCamelCase            =    output[0, -1, -3:, -3:]
         __lowerCamelCase            =    torch.tensor(a       ).to(a       )
         assert torch_all_close(output_slice.flatten()    ,							a    ,							atol=5e-3       )






     @unittest.skipIf(torch_device == '''mps'''    ,							'''Training is not supported in mps'''       )
     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase      ,    __lowerCamelCase            =    self.prepare_init_args_and_inputs_for_common()
         __lowerCamelCase            =    self.block_class(**a       )
         model.to(a       )
         model.train()
         __lowerCamelCase            =    model(**a       )

         if isinstance(a    ,							a       ):
             __lowerCamelCase            =    output[0]

         __lowerCamelCase            =    torch.device(a       )
         __lowerCamelCase            =    randn_tensor(output.shape    ,							device=a       )
         __lowerCamelCase            =    torch.nn.functional.mse_loss(a    ,							a       )
         loss.backward()
